AWS Marketplace
Publish algorithm on the AWS Marketplace
Create your algorithm and model package
- Building your own container as Algorithm / Model Package
- When should I build my own algorithm container?
- Permissions
- The example
- The presentation
- Part 1: Packaging and Uploading your Algorithm for use with Amazon SageMaker
- Part 2: Training, Batch Inference and Hosting your Algorithm in Amazon SageMaker
- Part 3 - Package your resources as an Amazon SageMaker Algorithm
- Part 4 - Package your resources as an Amazon SageMaker ModelPackage
- Part 5 - Package your resources as an Amazon SageMaker Algorithm for Fine-tuning Large Models
- Notebook CI Test Results
Curate your sample notebook
Templates
- Train, tune, and deploy a custom ML model using For Seller to update: Title_of_your_ML Algorithm Algorithm from AWS Marketplace
- Deploy For Seller to update: Title_of_your_ML Model Model Package from AWS Marketplace
Use AWS Data Exchange products
- Using Shutterstock’s Image datasets to train a multi-label image classification model
- Using dataset product from AWS Data Exchange with ML model from AWS Marketplace
- Notebook CI Test Results
Use AWS Marketplace algorithms
- AWS Marketplace Product Usage Demonstration - Algorithms
Autogluon
AutoML
ImplicitBPR
- Implementing a Recommender System for Implicit Feedback Datasets with Bayesian Personalized Ranking
- Classes of recommender systems:
- Explicit vs. Implicit Users Feedback:
- About this Notebook
- Step 1: Pre-requisites: subscribe to Implicit BPR Algorithm from AWS Marketplace
- Step 2: Set up environment
- Step 3: Data collection and preparation
- Step 4: Train the model and evaluate the performance metrics
- Step 5: Perform a batch/offline inference
- Step 6: Deploy the model and perform a real-time inference
- Step 7: Cleaning up the Resources
- Notebook CI Test Results
Use AWS Marketplace model packages
Auto insurance
- Goal: Automate Auto Insurance Claim Processing Using Pre-trained Models
Improving industrial workplace safety
- Demonstrating Industrial Workplace Safety using Pre-trained Machine Learning Models
- Introduction
- Pre-requisites
- Step 1: Set up environment and view sample images
- Step 2: Deploy construction worker detection model
- Step 3: Deploy the hard-hat detection model.
- Step 4. Deploy the Personal Protective Equipment (PPE) detection model
- Step 5. Deploy the Construction Machines detection model
- Step 6. Generate actionable insights on video input
- Step 7. Explore other relevant models!
- Step 8. Cleanup
- Notebook CI Test Results
Generic sample notebook
GPT-2 XL use cases
- Creative writing using GPT-2 Text Generation